Quantification of dynamic protein complexes using <i>Renilla</i> luciferase fragment complementation applied to protein kinase A activities <i>in vivo</i>
Bibliographic record
Abstract
The G protein-coupled receptor (GPCR) superfamily represents the most important class of pharmaceutical targets. Therefore, the characterization of receptor cascades and their ligands is a prerequisite to discovering novel drugs. Quantification of agonist-induced second messengers and downstream-coupled kinase activities is central to characterization of GPCRs or other pathways that converge on GPCR-mediated signaling. Furthermore, there is a need for simple, cell-based assays that would report on direct or indirect actions on GPCR-mediated effectors of signaling. More generally, there is a demand for sensitive assays to quantify alterations of protein complexes in vivo. We describe the development of a Renilla luciferase (Rluc)-based protein fragment complementation assay (PCA) that was designed specifically to investigate dynamic protein complexes. We demonstrate these features for GPCR-induced disassembly of protein kinase A (PKA) regulatory and catalytic subunits, a key effector of GPCR signaling. Taken together, our observations show that the PCA allows for direct and accurate measurements of live changes of absolute values of protein complex assembly and disassembly as well as cellular imaging and dynamic localization of protein complexes. Moreover, the Rluc-PCA has a sufficiently high signal-to-background ratio to identify endogenously expressed Galpha(s) protein-coupled receptors. We provide pharmacological evidence that the phosphodiesterase-4 family selectively down-regulates constitutive beta-2 adrenergic- but not vasopressin-2 receptor-mediated PKA activities. Our results show that the sensitivity of the Rluc-PCA simplifies the recording of pharmacological profiles of GPCR-based candidate drugs and could be extended to high-throughput screens to identify novel direct modulators of PKA or upstream components of GPCR signaling cascades.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".